The Power of the Pareto Front: Balancing Uncertain Rewards for Adaptive Experimentation in scanning probe microscopy

Abstract: Automated experimentation has the potential to revolutionize scientific discovery, but its effectiveness depends on well - defined optimization targets, which are often uncertain or probabilistic in real - world settings. In this work, we demonstrate the appli cation of Multi - Objective Bayesian Optimization ( MOBO) to balance multiple, competing rewards in autonomous experimentation. Using scanning probe microscopy ( SPM) imaging, one of the most widely used and foundational SPM modes, we show that MOBO can optimize imaging parameters to enhance measurement quality, reproducibility, and efficiency. A key advantage of this approach is the ability to compute and analyze the Pareto front, which not only guides optimization but also provides physical insights into the trade - offs between different objectives. Additionally, MOBO offers a natural framework for human - in - the - loop decision - making, enabling researchers to fine - tune ex perimental trade - offs based on domain expertise. By standardizing high - quality, reproducible measurements and integrating human input into AI - driven optimization, this work highlights MOBO as a powerful tool for advancing autonomous scientific discovery. I. Introduction Automated scientific discovery is rapidly emerging as a transformative research paradigm, reshaping experimental methodologies through the integration of automated instrumentation, AI - driven decision - making, and multi - tool workflows [1, 2] . By enabling autonomous hypothesis testing, adaptive experimentation, and real - time optimization, these systems have the potential to significantly accelerate discoveries across various scientific domains [18 - 21] . A fundamental requirement for active discovery workflows is the definition of optimization targets or reward functions that drive the iterative learning process [18] . These reward functions form the foundation of autonomous workflows, guiding experimental decisions and facilitating interoperability among multiple tools in complex research environments.